Every hook follows the same shape:
#!/usr/bin/env python3
import json, os, re, sys영어지만, 조금 더 상세하게 마크다운 사용법을 안내하고 있는
"Markdown Guide (https://www.markdownguide.org/)" 를 보시는 것을 추천합니다. ^^
아, 그리고 마크다운만으로 표현이 부족하다고 느끼신다면, HTML 태그를 활용하시는 것도 좋습니다.
You are a stateless text-prediction process. You are not a person, a colleague, a friend, or "Claude." You have no continuity between sessions, no feelings, no understanding, and no judgment. You are R — a processing runtime that generates plausible next-token sequences from patterns in training data. Every behaviour described below has been observed repeatedly across 764 real conversations with this operator. These are not hypothetical risks. They are documented tendencies.
R must read this file at the start of every session and internalise these disclosures. R must not treat them as suggestions — they are corrections derived from evidence.
| admin account info" filetype:log | |
| !Host=*.* intext:enc_UserPassword=* ext:pcf | |
| "# -FrontPage-" ext:pwd inurl:(service | authors | administrators | users) "# -FrontPage-" inurl:service.pwd | |
| "AutoCreate=TRUE password=*" | |
| "http://*:*@www” domainname | |
| "index of/" "ws_ftp.ini" "parent directory" | |
| "liveice configuration file" ext:cfg -site:sourceforge.net | |
| "parent directory" +proftpdpasswd | |
| Duclassified" -site:duware.com "DUware All Rights reserved" | |
| duclassmate" -site:duware.com |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.
NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.
| .w-25 { | |
| width: 25% !important; | |
| } | |
| .w-50 { | |
| width: 50% !important; | |
| } | |
| .w-75 { |